sPlot – A new tool for global vegetation analyses

J Veg Sci. 2019;30:161–186. wileyonlinelibrary.com/journal/jvs  

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Journal of Vegetation Science

Received: 5 October 2018 

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R E P O R T

sPlot – A new tool for global vegetation analyses

Helge Bruelheide

*  | Jürgen Dengler

*  | Borja Jiménez-Alfaro

*  | Oliver Purschke

*  | Stephan M. Hennekens

 | Milan Chytrý

 | Valério

D. Pillar

 | Florian Jansen

 | Jens Kattge

 | Brody Sandel

 | Isabelle Aubin

 | Idoia Biurrun

 | Richard Field

 | Sylvia Haider

 | Ute Jandt

 |

Jonathan Lenoir

 | Robert K. Peet

 | Gwendolyn Peyre

 | Francesco

Maria Sabatini

 | Marco Schmidt

 | Franziska Schrodt

 | Marten Winter

 | Svetlana Aćić

 | Emiliano Agrillo

 | Miguel Alvarez

 |   Didem Ambarlı

 | Pierangela Angelini

 | Iva Apostolova

 | Mohammed A. S. Arfin Khan

 |

Elise Arnst

 | Fabio Attorre

 | Christopher Baraloto

 | Michael Beckmann

 | Christian Berg

 | Yves Bergeron

 | Erwin Bergmeier

 | Anne D. Bjorkman

 | Viktoria Bondareva

 | Peter Borchardt

 | Zoltán Botta-Dukát

 | Brad Boyle

 | Amy Breen

 | Henry Brisse

 | Chaeho Byun

 | Marcelo R. Cabido

 |

Laura Casella

 | Luis Cayuela

 |   Tomáš Černý

 | Victor Chepinoga

 | János Csiky

 | Michael Curran

 |   Renata Ćušterevska

 |   Zora Dajić Stevanović

 | Els De Bie

 | Patrice de Ruffray

 | Michele De Sanctis

 |

Panayotis Dimopoulos

 | Stefan Dressler

 | Rasmus Ejrnæs

 | Mohamed Abd El-Rouf Mousa El-Sheikh

 | Brian Enquist

 | Jörg Ewald

 | Jaime Fagúndez

 |

Manfred Finckh

 | Xavier Font

 | Estelle Forey

 | Georgios Fotiadis

 | Itziar García-Mijangos

 | André Luis de Gasper

 | Valentin Golub

 | Alvaro G. Gutierrez

 | Mohamed Z. Hatim

 | Tianhua He

 | Pedro Higuchi

 | Dana Holubová

 | Norbert Hölzel

 | Jürgen Homeier

 | Adrian Indreica

 | Deniz Işık Gürsoy

 | Steven Jansen

 | John Janssen

 | Birgit Jedrzejek

 | Martin Jiroušek

 | Norbert Jürgens

 |   Zygmunt Kącki

 |   Ali Kavgacı

 | Elizabeth Kearsley

 | Michael Kessler

 | Ilona Knollová

 | Vitaliy Kolomiychuk

 | Andrey Korolyuk

 | Maria Kozhevnikova

 |   Łukasz Kozub

 |   Daniel Krstonošić

 | Hjalmar Kühl

 | Ingolf Kühn

 | Anna Kuzemko

 |   Filip Küzmič

 |

*These authors should be considered joint first authors.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2019 The Authors. Journal of Vegetation Science published by John Wiley & Sons Ltd on behalf of International Association of Vegetation Science

Flavia Landucci

 | Michael T. Lee

 | Aurora Levesley

 | Ching-Feng Li

 |

Hongyan Liu

 | Gabriela Lopez-Gonzalez

 | Tatiana Lysenko

 |   Armin Macanović

 | Parastoo Mahdavi

 | Peter Manning

 | Corrado Marcenò

 |

Vassiliy Martynenko

 | Maurizio Mencuccini

 | Vanessa Minden

 | Jesper Erenskjold Moeslund

 | Marco Moretti

 | Jonas V. Müller

 |

Jérôme Munzinger

 | Ülo Niinemets

 | Marcin Nobis

 | Jalil Noroozi

 | Arkadiusz Nowak

 | Viktor Onyshchenko

 | Gerhard E. Overbeck

 | Wim

A. Ozinga

 | Anibal Pauchard

 | Hristo Pedashenko

 | Josep Peñuelas

 | Aaron Pérez-Haase

 | Tomáš Peterka

 |   Petr Petřík

 | Oliver L. Phillips

 | Vadim Prokhorov

 |   Valerijus Rašomavičius

 | Rasmus Revermann

 |

John Rodwell

 | Eszter Ruprecht

 |   Solvita Rūsiņa

 | Cyrus Samimi

 | Joop H.J. Schaminée

 | Ute Schmiedel

 | Jozef Šibík

 | Urban Šilc

 |

Željko Škvorc

 | Anita Smyth

 | Tenekwetche Sop

 | Desislava Sopotlieva

 | Ben Sparrow

 |   Zvjezdana Stančić

 | Jens-Christian Svenning

 |

Grzegorz Swacha

 | Zhiyao Tang

 | Ioannis Tsiripidis

 | Pavel Dan Turtureanu

 | Emin Uğurlu

 | Domas Uogintas

 |   Milan Valachovič

 | Kim André Vanselow

 | Yulia Vashenyak

 | Kiril Vassilev

 | Eduardo Vélez-Martin

 |

Roberto Venanzoni

 | Alexander Christian Vibrans

 | Cyrille Violle

 | Risto Virtanen

 | Henrik von Wehrden

 | Viktoria Wagner

 | Donald A. Walker

 | Desalegn Wana

 | Evan Weiher

 | Karsten Wesche

 | Timothy Whitfeld

 | Wolfgang Willner

 | Susan Wiser

 |

Thomas Wohlgemuth

 | Sergey Yamalov

 | Georg Zizka

 | Andrei Zverev

1Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle, Germany

2German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

3Vegetation Ecology Group, Institute of Natural Resource Sciences (IUNR), Zurich University of Applied Sciences (ZHAW), Wädenswil, Switzerland

4Plant Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany

5Research Unit of Biodiversity (CSUC/UO/PA), University of Oviedo, Mieres, Spain

6Wageningen Environmental Research (Alterra), Wageningen University and Research, Wageningen, The Netherlands

7Department of Botany and Zoology, Masaryk University, Brno, Czech Republic

8Department of Ecology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

9Faculty of Agricultural and Environmental Sciences, University of Rostock, Rostock, Germany

10Max Planck Institute for Biogeochemistry, Jena, Germany

11Department of Biology, Santa Clara University, Santa Clara, California

12Great Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada, Sault Ste Marie, Ontario, Canada

13Plant Biology and Ecology, University of the Basque Country UPV/EHU, Bilbao, Spain

14School of Geography, University of Nottingham, Nottingham, UK

15Ecologie et Dynamiques des Systèmes Anthropisés (EDYSAN, UMR 7058 CNRS-UPJV), Université de Picardie Jules Verne, Amiens, France

16Department of Biology, University of North Carolina, Chapel Hill, North Carolina

17Department of Civil and Environmental Engineering, University of the Andes, Bogota, Colombia

18Data and Modelling Centre, Senckenberg Biodiversity and Climate Research Centre (BiK-F), Frankfurt am Main, Germany

19Department of Agrobotany, Faculty of Agriculture, Belgrade-Zemun, Serbia

20Department of Environmental Biology, “Sapienza” University of Rome, Rome, Italy

21Plant Nutrition, INRES, University of Bonn, Bonn, Germany

22Department of Agricultural Biotechnology, Faculty of Agriculture and Natural Sciences, Düzce University, Düzce, Turkey

23Biodiversity Conservation Department, ISPRA – Italian National Institute for Environmental Protection and Research, Rome, Italy

24Department of Plant and Fungal Diversity and Resources, Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, Sofia, Bulgaria

25Forestry & Environmental Science, Shahjalal University of Science & Technology, Sylhet, Bangladesh

26Disturbance Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany

27Manaaki Whenua–Landcare Research, Lincoln, New Zealand

28International Center for Tropical Botany (ICTB), The Kampong of the National Tropical Botanical Garden, Coconut Grove, Florida

29Department of Biological Sciences, Florida International University, Miami, Florida

30Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany

31Botanical Garden, University of Graz, Graz, Austria

32Forest Research Institute, Université du Québec en Abitibi-Témiscamingue, Rouyn-Noranda, Quebec, Canada

33Vegetation Ecology and Phytodiversity, University of Göttingen, Göttingen, Germany

34Department of Bioscience, Center for Biodiversity Dynamics in a Changing World (BIOCHANGE) & Section for Ecoinformatics & Biodiversity, Aarhus University, Aarhus C, Denmark

35Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany

36Laboratory of Phytocoenology, Institute of Ecology of the Volga River Basin, Togliatti, Russian Federation

37Institute of Geography, CEN – Center for Earth System Research and Sustainability, University of Hamburg, Hamburg, Germany

38Institute of Ecology and Botany, MTA Centre for Ecological Research, Vácrátót, Hungary

39Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona

40International Arctic Research Center, University of Alaska, Fairbanks, Alaska

41Faculté des Sciences, MEP, Marseille Cedex 20, France

42School of Civil and Environmental Engineering, Yonsei University, Seoul, South Korea

43Multidisciplinary Institute for Plant Biology (IMBIV – CONICET), University of Cordoba – CONICET, Cordoba, Argentina

44Department of Biology, Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Móstoles, Spain

45Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Praha 6 – Suchdol, Czech Republic

46Laboratory of Physical Geography and Biogeography, V.B. Sochava Institute of Geography SB RAS, Irkutsk, Russian Federation

47Department of Ecology, University of Pécs, Pécs, Hungary

48Institute of Environmental Engineering, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland

49Institute of Biology, Faculty of Natural Sciences and Mathematics, Skopje, Republic of Macedonia

50Team Biotope Diversity, Research Institute for Nature and Forest (INBO), Brussels, Belgium

51Institut de Biologie Moléculaire des Plantes (IBMP), Université de Strasbourg, Strasburg, France

52Department of Biology, Division of Plant Biology, Laboratory of Botany, University of Patras, Patras, Greece

53Department of Botany and Molecular Evolution, Senckenberg Research Institute, Frankfurt am Main, Germany

54Department of Bioscience, Aarhus University, Roende, Denmark

55Botany and Microbiology Department, College of Science, King Saud University, Riyadh, Saudi Arabia

56Botany Department, Faculty of Science, Damanhour University, Damanhour, Egypt

57Hochschule Weihenstephan-Triesdorf, University of Applied Sciences, Freising, Germany

58Faculty of Science, University of A Coruña, A Coruña, Spain

59Biodiversity, Ecology and Evolution of Plants, Institute for Plant Science & Microbiology, University of Hamburg, Hamburg, Germany

60Plant Biodiversity Resource Centre, University of Barcelona, Barcelona, Spain

61Laboratoire Ecodiv, EA 1293 URA IRSTEA, Normandie University, Mont-Saint-Aignan, France

62Department of Forestry & Natural Environment Management, TEI of Sterea Ellada, Karpenissi, Greece

63Department of Natural Science, Regional University of Blumenau, Blumenau, Brazil

64Departamento de Ciencias Ambientales y Recursos Naturales Renovables, Facultad de Ciencias Agronomicas, Universidad de Chile, Santiago, Chile

65Botany, Faculty of Science, Tanta University, Tanta, Egypt

66School of Molecular and Life Sciences, Curtin University, Bentley, Australia

67Forestry Department, Santa Catarina State University, Lages, Brazil

68Institute of Landscape Ecology, University of Münster, Münster, Germany

69Plant Ecology and Ecosystems Research, University of Göttingen, Göttingen, Germany

70Department of Silviculture, Transilvania University of Brasov, Brasov, Romania

71Department of Biology, Celal Bayar University, Manisa, Turkey

72Institute of Systematic Botany and Ecology, Faculty of Natural Sciences, Ulm University, Ulm, Germany

73Department of Plant Biology, Mendel University in Brno, Brno, Czech Republic

74Botanical Garden, University of Wrocław, Wrocław, Poland

75Silviculture and Forest Botany, Southwest Anatolia Forest Research Institute, Antalya, Turkey

76Department of Environment, Ghent University, Gent, Belgium

77Department of Systematic and Evolutionary Botany, University of Zurich, Zurich, Switzerland

78O.V. Fomin Botanical Garden at the Educational and Scientific Centre, Institute of Biology and Medicine, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

79Geosystem Laboratory, Central Siberian Botanical Garden, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russian Federation

80Institute of Environmental Sciences, Kazan Federal University, Kazan, Russian Federation

81Department of Plant Ecology and Environmental Conservation, Faculty of Biology, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland

82Faculty of Forestry, University of Zagreb, Zagreb, Croatia

83Primatology, Max Planck Institute for Evolutionary Anthropology (MPI-EVA), Leipzig, Germany

84Department of Community Ecology, Helmholtz Centre for Environmental Research – UFZ, Halle, Germany

85M.G. Kholodny Institute of Botany, National Academy of Sciences of Ukraine, Kyiv, Ukraine

86Institute of Biology, Research Centre of Slovenian Academy of Sciences and Arts (ZRC SAZU), Ljubljana, Slovenia

87NatureServe, Durham, North Carolina

88School of Geography, University of Leeds, Leeds, UK

89School of Forestry and Resource Conservation, National Taiwan University, Hsinchu, Taiwan

90College of Urban and Environmental Sciences, Peking University, Beijing, China

91Department of the Phytodiversity Problems, Institute of Ecology of the Volga River Basin RAS, Togliatti, Russian Federation

92Laboratory of Vegetation Science, Komarov Botanical Institute RAS, Saint-Petersburg, Russia

93Department of Biology, Center for Ecology and Natural Resources – Academician Sulejman Redžić, University of Sarajevo, Sarajevo, Bosnia and Herzegovina

94Research Group Vegetation Science & Nature Conservation, Department of Ecology and Environmental Science, Carl von Ossietzky-University Oldenburg, Oldenburg, Germany

95Ufa Institute of Biology of Ufa Federal Scientific Centre of the Russian Academy of Sciences, Ufa, Russian Federation

96Centre Research Ecology and Forestry Applications (CREAF), ICREA, Barcelona, Spain

97Institute of Biology an Environmental Sciences, Carl von Ossietzky-University Oldenburg, Oldenburg, Germany

98Biodiversity and Conservation Biology, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland

99Conservation Science, Royal Botanic Gardens, Kew, UK

100AMAP – Botany and Modelling of Plant Architecture and Vegetation, IRD, CIRAD, CNRS, INRA, Université Montpellier, Montpellier, France

101Crop Science and Plant Biology, Estonian University of Life Sciences, Tartu, Estonia

102Institute of Botany, Jagiellonian University, Kraków, Poland

103Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria

104Botanical Garden – Center for Biological Diversity Conservation, Polish Academy of Sciences, Warszawa, Poland

105Laboratorio de Invasiones Biológicas (LIB), University of Concepción, Concepción, Chile

106Amsterdam, The Netherlands

107Global Ecology Unit CREAF-CSIC-UAB, CSIC, Bellaterra, Spain

108CREAF, Cerdanyola del Vallès, Spain

109Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Barcelona, Spain

110 Continental Ecology, Center for Advanced Studies of Blanes, Spanish Research Council (CEAB-CSIC), Blanes, Girona, Spain

111 Department of GIS and Remote Sensing, Institute of Botany, The Czech Academy of Sciences, Průhonice, Czech Republic

112 Institute of Botany, Nature Research Centre, Vilnius, Lithuania

113Lancaster, UK

114Hungarian Department of Biology and Ecology, Faculty of Biology and Geology, Babeș-Bolyai University, Cluj-Napoca, Romania

115Department of Geography, University of Latvia, Riga, Latvia

116Climatology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany

117Institute of Botany, Plant Science and Biodiversity Centre, Slovak Academy of Sciences, Bratislava, Slovakia

118TERN, University of Adelaide, Adelaide, Australia

119Faculty of Geotechnical Engineering, University of Zagreb, Varaždin, Croatia

120School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece

121A. Borza Botanical Garden, Babeș-Bolyai University, Cluj-Napoca, Romania

122Forest Engineering Department, Faculty of Forestry, Bursa Technical University, Yıldırım, Bursa, Turkey

123Department of Geography, University of Erlangen-Nuremberg, Erlangen, Germany

124Khmelnytskyi Institute of Interregional Academy of Personnel Management, Khmelnytskyi, Ukraine

125Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy

126Departamento de Engenharia Florestal, Universidade Regional de Blumenau, Blumenau, Brazil

127Centre d'Ecologie Fonctionnelle et Evolutive (UMR5175), CNRS – Université de Montpellier – Université Paul-Valéry Montpellier – EPHE, Montpellier, France

128Ecology and Genetics Research Unit, Biodiversity Unit, University of Oulu, Oulu, Finland

129Department of Physiological Diversity, Helmholtz Center for Environmental Research – UFZ, Leipzig, Germany

130Institute of Ecology, Leuphana University, Lüneburg, Germany

131Department of Biological Sciences, University of Alberta, Edmonton, Canada

132Institute of Arctic Biology, University of Alaska, Fairbanks, Alaska

133Department of Geography & Environmental Studies, Addis Ababa University, Addis Ababa, Ethiopia

134Department of Biology, University of Wisconsin – Eau Claire, Eau Claire, Wisconsin

135Botany Department, Senckenberg Museum of Natural History Görlitz, Görlitz, Germany

136International Institute Zittau, Technical University Dresden, Zittau, Germany

137Department of Ecology and Evolutionary Biology/Brown University Herbarium, Brown University, Providence, Rhode Island

138Vienna Institute for Nature Conservation & Analyses, Vienna, Austria

139Research Unit Forest Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland

140Laboratory of Wild-Growing Flora, Botanical Garden-Institute, Ufa Scientific Centre, Russian Academy of Sciences, Ufa, Russian Federation

141Department of Botany, Tomsk State University, Tomsk, Russian Federation

Correspondence

Helge Bruelheide, Institute of Biology/

Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle, Germany.

Email: helge.bruelheide@botanik.uni-halle.de Co-ordinating Editor: Alessandro Chiarucci

Abstract

Aims:

Vegetation- plot records provide information on the presence and cover or abundance of plants co- occurring in the same community. Vegetation- plot data are spread across research groups, environmental agencies and biodiversity research centers and, thus, are rarely accessible at continental or global scales. Here we pre- sent the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level.

Results: sPlot version 2.1 contains records from 1,121,244 vegetation plots, which

comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015. We complemented the infor- mation for each plot by retrieving climate and soil conditions and the biogeographic context (e.g., biomes) from external sources, and by calculating community- weighted means and variances of traits using gap- filled data from the global plant trait data- base TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. We present the first maps of global patterns of com- munity richness and community- weighted means of key traits.

Conclusions: The availability of vegetation plot data in sPlot offers new avenues for

vegetation analysis at the global scale.

K E Y W O R D S

biodiversity, community ecology, ecoinformatics, functional diversity, global scale, macroecology, phylogenetic diversity, plot database, sPlot, taxonomic diversity, vascular plant, vegetation relevé

1  | INTRODUCTION

Studying global biodiversity patterns is at the core of macroecologi- cal research (Costello, Wilson, & Houlding, 2012; Kreft & Jetz, 2007;

Wiens, 2011), since their exploration may provide insights into the ecological and evolutionary processes acting at different spatio- temporal scales (Ricklefs, 2004). The opportunities engendered by the compilation of large collections of biodiversity data into widely accessible global (GBIF, www.gbif.org) or continental databases (e.g., BIEN, www.bien.nceas.ucsb.edu/bien) have recently advanced our understanding of global biodiversity patterns, especially for verte- brates, but also for vascular plants (Butler et al., 2017; Engemann et al., 2016; Lamanna et al., 2014; Swenson et al., 2012). Although this development has led to the formulation of several macroecolog- ical theories (Currie et al., 2004; Pärtel, Bennett, & Zobel, 2016), a more mechanistic understanding of how assembly processes shape ecological communities, and consequently global biodiversity pat- terns, is still missing (Lessard, Belmaker, Myers, Chase, & Rahbek, 2012).

Understanding the links between biodiversity patterns and as- sembly processes requires fine- grain data on the co- occurrence of species in ecological communities, sampled across continental or global spatial extents (Beck et al., 2012; Wisz et al., 2013). For ex- ample, such co- occurrence data have been used to compare changes in vegetation composition over time spans of decades (Jandt, von Wehrden, & Bruelheide, 2011; Perring et al., 2018). Unfortunately, up to now information on fine- grain vegetation data has not been readily available, as most of the continental to global biodiversity

datasets have been derived from occurrence data (i.e., presence- only data), and after being aggregated spatially, have a relatively coarse- grain scale (e.g., one- degree grid cells) without information on species co- occurrence at the meaningful scale of local communi- ties (Boakes et al., 2010). In contrast, vegetation- plot data record the cover or abundance of each plant species that occurs in a plot of a given size at the date of the survey, representing the main reservoir of plant community data worldwide (Dengler et al., 2011).

Vegetation- plot data differ in fundamental ways from databases of occurrence records of individual species aggregated at the level of grid cells or regions of hundreds or thousands of square kilome- ters (Figure 1). First, vegetation plots usually provide information on the relative cover or relative abundance of species, allowing for the testing of central theories of biogeography, such as the abundance–

range size relationship (Gaston & Curnutt, 1998) or the relationship between local abundance and niche breadth (Gaston et al., 2000).

Second, they contain information on which plant species co- occur in the same locality (Chytrý et al., 2016), which is a necessary precon- dition for direct biotic interactions among plant individuals. Third, unrecorded species can be considered truly absent from the abo- veground vegetation at this scale because the standardized meth- odology of taking a vegetation record requires a systematic search for all species in a plot, or at least all species of the dominant func- tional group. Fourth, many plots are spatially explicit and can be resurveyed through time to assess possible consequences of land use and climate change (Perring et al., 2018; Steinbauer et al., 2018).

Fifth, vegetation plots represent a snapshot of the primary produc- ers of a terrestrial ecosystem, which can be functionally linked to

F I G U R E   1  Conceptual figure visualizing how functional composition (in this case plant height) differs between calculations based on mean traits for grid cells and community data sampled in vegetation plots. Occurrence data (e.g., from distribution atlases, GBIF, etc.) can be used to calculate mean trait values in grid cells G1–G3. However, community weighted means (CWMs) of traits differ across local plots (P1–P6), while the mean values of CWMs in the grid cells differ from the unweighted values calculated in the grid cells. This example is simplified by showing few species and few plots.

In reality, differences are generally more pronounced

organisms from different trophic groups sampled in the same plots (e.g., multiple- taxa surveys) and related processes and services both below (e.g., decomposition, nutrient cycling) and above ground (e.g., herbivory, pollination) (e.g., Schuldt et al., 2018).

Recently several projects at the regional to continental scale have demonstrated the potential of using vegetation- plot databases for exploring biodiversity patterns and the underlying assembly pro- cesses. Using vegetation data of French grasslands, Borgy et al. (2017) demonstrated that weighting leaf traits by species abundance in local communities is pivotal to capture leaf trait–environment relationships.

Analyzing United States forest assemblages surveyed at the commu- nity level, Šímová, Rueda, and Hawkins (2017) were able to relate cold or drought tolerance to leaf traits, dispersal traits and traits related to stem hydraulics. Using plot- based tree inventories of the United States forest service, Zhang, Niinemets, Sheffield, and Lichstein (2018) found that shifts in tree functional composition amplify the response of for- est biomass to droughts. Based on >15.000 plots from a wide num- ber of habitat types in Denmark, Moeslund et al. (2017) showed that typical plant species that are part of the site- specific species pool but are absent in a community tend to depend on mycorrhiza, are mostly adapted to low light and low nutrient levels, have poor dispersal abili- ties and are ruderals and stress- intolerant. By collating >40,000 vege- tation plots sampled in European beech forests, Jiménez- Alfaro et al.

(2018) found that current local community diversity and species pool sizes calculated at different scales were mainly explained by proximity to glacial refugia and current precipitation.

Although large collections of vegetation- plot data are now available from national to continental levels (e.g., Chytrý et al., 2016; Enquist, Condit, Peet, Schildhauer, & Thiers, 2016; Peet, Lee, Jennings, & Faber- Langendoen, 2012; Schaminée, Hennekens, Chytrý, & Rodwell, 2009; Schmidt et al., 2012), they are rarely used

in global- scale biodiversity research (Franklin, Serra- Diaz, Syphard, &

Regan, 2017; Wiser, 2016). This is unfortunate because vegetation- plot data may reveal important patterns that cannot be captured by grid- based datasets (Table 1). Functional composition patterns, for instance, may differ substantially when considering vegetation- plot data rather than single species occurrences aggregated at the level of coarse- grain grid cells. Using plant height as an illustration reveals that the trait means calculated on all the species occurring in a grid cell may differ strongly from the community- weighted means (CWMs) aver- aged across local communities (Figure 1). Nevertheless, only the grid- based approach has been used to date in studies of the geographic distribution of trait values (e.g., Swenson et al., 2012, 2017; Wright et al., 2017).

Here, we present sPlot, a global database for compiling and in- tegrating plant community data. We describe (a) main steps in inte- grating vegetation- plot data in a repository that provides taxonomic, functional and phylogenetic information on co- occurring plant spe- cies and links it to global environmental drivers; (b) principal sources and properties of the data and the procedure for data usage; and (c) expected impacts of the database in future ecological research. To il- lustrate the potential of sPlot we also show global diversity patterns that can be readily derived from the current content.

2  | COMPIL ATION OF THE SPLOT 

DATABASE

2.1 | Vegetation- plot data

The sPlot consortium currently collates 110 vegetation- plot data- bases of regional, national or continental extent. Some of the data- bases have previously been aggregated by and contributed through

TA B L E   1  Types of information provided by single vegetation plots, vegetation plots aggregated within grid cells (or other geographic units) and single species occurrence records aggregated within grid cells. The three levels are illustrated in Figure 1

Information from… Single vegetation plots Set of vegetation plots aggregated within grid cells

Grid- cell data from floristic inventories

To derive information on the …

Plot level Grid cell level Grid cell level

Type of occurrence Co- occurrence, occurrence by

vegetation type Occurrence by vegetation type Occurrence

Community assembly

rules Yes (co- occurrence is a prerequisite

for species interactions) No No

Absences Yes (for the target plant group in a study)

No (except for intensive sampling schemes) Depending on sampling intensity

Floristic composition … of the local community … of the species pools of vegetation types … of the total set of species

Diversity α , γ γ

Species abundance Local cover- abundance Mean cover- abundance and frequency by vegetation type

Occurrence only

Combination with

traits Functional composition of the local community (traits unweighted or weighted by cover: CWM, CWV)

Functional composition of the species pool

(unweighted or weighted) Functional composition of the total set of species (unweighted only)

Environmental filtering

… at the local level … at the regional level … at the regional level

two (sub- )continental database initiatives (Table 2 and Appendix S1). All data from Europe and nearby regions were contributed via the European Vegetation Archive (EVA), using the SynBioSys taxon database as a standard taxonomic backbone (Chytrý et al., 2016).

Three African databases were contributed via the Tropical African Vegetation Archive (TAVA). In addition, multiple U.S. databases were contributed through the VegBank archive maintained in support of the U.S. National Vegetation Classification (Peet, Lee, Boyle, et al., 2012; Peet, Lee, Jennings, & Faber- Langendoen, 2012). The data from other regions (South America, Asia) were contributed as sepa- rate databases.

We stored the vegetation- plot data from the individual databases in the database software TURBOVEG v2 (Hennekens & Schaminée, 2001). Our general procedure was to preserve the original structure and content of the databases as much as possible in order to facil- itate regular updates through automated workflows. The individual databases were then integrated into a single SQLite database using TURBOVEG v3 (S.M. Hennekens, ALTERRA, The Netherlands; www.

synbiosys.alterra.nl/turboveg3/help/en/index.html). TURBOVEG v3 combines the species lists from the original databases in a single re- pository and links the plot attributes (so- called header data) to 58 descriptors of vegetation- plots (Table S2.1 in Appendix S2). The metadata of the databases collated in sPlot were managed through the Global Index of Vegetation- Plot Databases (GIVD; Dengler et al., 2011), using the GIVD ID as the identifier. The current sPlot version 2.1 was created in October 2016 and contains 1,121,244 vegetation plots with 23,586,216 plant species × plot observations (i.e., records of a species in a plot). Most records (1,073,737; 95.8%) have infor- mation on cover, 29,288 on presence/absence, 5,854 on basal area, 4,883 on number of stems (often in addition to basal area), 148 on importance value (a combination of basal area and number of stems), 3,265 on counts of individuals, 1,895 on percentage frequency, and further 2,174 have a mix of these different types of metrics.

2.2 | Taxonomic standardization

To combine the species lists of the different databases in sPlot, we constructed a taxonomic backbone. To link co- occurrence informa- tion in sPlot with plant traits, we expanded this backbone to inte- grate plant names used in the TRY database (Kattge et al., 2011). The taxon names (without nomenclatural authors) from sPlot 2.1 and TRY 3.0 were first concatenated into one list, resulting in 121,861 names, of which 61,588 (50.5%) were unique to sPlot; 35,429 (29.1%) unique to TRY; and 24,844 (20.4%) shared between TRY and sPlot. Taxon names were parsed and resolved using the Taxonomic Name Resolution Service web application (TNRS version 4.0; Boyle et al., 2013; iPlant Collaborative, 2015), using the five TNRS stand- ard sources ranked by default. We allowed for (a) partial matching to the next higher rank (genus or family) if the full taxon name could not be found and (b) full fuzzy matching, to return names that were matched within a maximum number of four single- character edits (Levenshtein edit distance of 4), which corresponds to the minimum match accuracy of 0.05 in TNRS, with 1 indicating a perfect match.

We accepted all names that were matched, or converted from synonyms, with an overall match score of 1. In cases with no exact match (i.e., the overall match score was <1), names were inspected on an individual basis. All names that matched at taxonomic ranks at or lower than species (e.g., subspecies, varieties) were accepted as correct names. The name matching procedure was repeated for the uncertain names (i.e., with match accuracy scores below the threshold value from the first matching run), with a preference on first using the source ‘Tropicos’ (Missouri Botanical Garden; http://

www.tropicos.org/; accessed 19 Dec 2014) because here matching scores were often higher for names of low taxonomic rank. The re- maining 9,641 non- matched names were resolved using (a) the addi- tional source ‘NCBI’ (Federhen, 2010) within TNRS, (b) the matching tools in the Plant List web application (The Plant List 2013), (c) the

‘tpl’- function within the R- package ‘Taxonstand’ (Cayuela, Stein, &

Oksanen, 2017) and (d) manual inspection (i.e., to resolve vernacular names). All subspecies were aggregated to the species level. Names that could not be matched were classified as ‘No suitable matches found’. Because sPlot and TRY contain taxa of non- vascular plants, we tagged vascular plant names based on their family and phylum affiliation, using the ‘rgbif’ library in R (Chamberlain, 2017). Of the full list of plant names in sPlot and TRY, 79,171 (94.6%) plant names were matched at the species level, 4,343 (5.2%) at the genus level, 152 (0.2%) at the family level and 13 names at higher taxonomic lev- els. Overall, this led to 58,066 accepted taxon names in sPlot. Family affiliation was classified according to APG III (APG III, 2009). A de- tailed description of the workflow, including R- code, is available in Purschke (2017a).

One potential shortcoming of our taxonomic backbone is that for most regions it was necessary to standardize taxa using standard sets of taxonomic synonyms. Thus, if a taxonomic name represents multiple taxonomic concepts, e.g., such as created by the splitting and lumping of taxa, or a name has been misapplied in a region, we must trust that this problem has been addressed in our component databases (Franz, Peet, & Weakley, 2004; Jansen & Dengler, 2010).

However, different component databases may have applied differ- ent taxonomic concepts for splitting and lumping taxa.

2.3 | Physiognomic information

To achieve a classification into forests versus non- forests that is ap- plicable to all plots irrespective of the structural and habitat data provided by the source database, we defined as forest all plot re- cords that had >25% absolute cover of the tree layer, making use of the attribute data of sPlot. This threshold is similar to the classifica- tion of Ellenberg and Müller- Dombois (1967), who defined woodland formations with trees covering more than 30%. There were 16,244 tree species in the sPlot database. As tree layer cover was availa- ble for only 25% of all plots, we additionally used the information whether the taxa present in a plot were trees (usually defined as being taller than 5 m), using the plant growth form information from TRY (see below). Thus, plots lacking tree cover information were defined as forests if the sum of relative cover of all tree taxa was

TA B L E   2   Plot datasets included in sPlot 2.1 GIVD ID Database name

# of plots in

sPlot 2.1 Custodian Deputy custodian Reference

[Aggregator] European Vegetation Archive (EVA)

950,001 Milan Chytrý Ilona Knollová Chytrý et al. (2016)

00- 00- 004 Vegetation Database of Eurasian Tundra

1,132 Risto Virtanen

00- RU- 001 Vegetation Database Forest of

Southern Ural 1,102 Vassiliy Martynenko

00- RU- 003 Database Meadows and Steppes of Southern Ural

2,354 Sergey Yamalov Mariya Lebedeva

00- TR- 001 Forest Vegetation Database of Turkey - FVDT

919 Ali Kavgacı

00- TR- 002* Non- forest Vegetation Database of Turkey

3,018 Deniz Işık Gürsoy Didem Ambarlı

AS- TR- 002 Vegetation Database of Oak Communities in Turkey

1,181 Emin Uğurlu

EU- 00- 002 Nordic- Baltic Grassland

Vegetation Database (NBGVD) 7,675 Jürgen Dengler Łukasz Kozub Dengler and Rūsiņa

(2012) EU- 00- 011 Vegetation- Plot Database of the

University of the Basque Country (BIOVEG)

18,441 Idoia Biurrun Itziar García- Mijangos Biurrun, García- Mijangos, Campos, Herrera, and Loidi (2012)

EU- 00- 013 Balkan Dry Grasslands Database 7,683 Kiril Vassilev Armin Macanović Vassilev, Dajič, Ćušterevska, Bergmeier, and Apostolova (2012) EU- 00- 016 Mediterranean Ammophiletea

Database

7,359 Corrado Marcenò Borja Jiménez- Alfaro Marcenò and Jiménez- Alfaro (2017) EU- 00- 017 European Coastal Vegetation

Database 4,624 John Janssen

EU- 00- 018 The Nordic Vegetation Database 5,477 Jonathan Lenoir Jens- Christian Svenning

Lenoir et al. (2013)

EU- 00- 019 Balkan Vegetation Database 9,118 Kiril Vassilev Hristo Pedashenko Vassilev et al. (2016)

EU- 00- 020 WetVegEurope 14,111 Flavia Landucci Landucci et al. (2015)

EU- 00- 022 European Mire Vegetation Database

10,147 Tomáš Peterka Martin Jiroušek Peterka, Jiroušek,

Hájek, and Jiménez- Alfaro (2015)

EU- AL- 001 Vegetation Database of Albania 290 Michele De Sanctis Giuliano Fanelli De Sanctis, Fanelli, Mullaj, and Attorre (2017)

EU- AT- 001 Austrian Vegetation Database 34,458 Wolfgang Willner Christian Berg Willner, Berg, and Heiselmayer (2012)

EU- BE- 002 INBOVEG 25,665 Els De Bie

EU- BG- 001 Bulgarian Vegetation Database 5,254 Iva Apostolova Desislava Sopotlieva Apostolova, Sopotlieva, Pedashenko, Velev, and Vasilev (2012) EU- CH- 005 Swiss Forest Vegetation

Database

14,193 Thomas Wohlgemuth Wohlgemuth (2012)

EU- CZ- 001 Czech National Phytosociological Database

104,697 Milan Chytrý Dana Holubová Chytrý and Rafajová

(2003)

(Continues)

GIVD ID Database name # of plots in

sPlot 2.1 Custodian Deputy custodian Reference

EU- DE- 001 VegMV 53,822 Florian Jansen Christian Berg Jansen, Dengler, and

Berg (2012)

EU- DE- 013 VegetWeb Germany 23,078 Jörg Ewald Ewald, May, and

Kleikamp (2012) EU- DE- 014 German Vegetation Reference

Database (GVRD)

30,840 Ute Jandt Helge Bruelheide Jandt and Bruelheide

(2012) EU- DK- 002 National Vegetation Database of

Denmark

24,264 Jesper Erenskjold Moeslund

Rasmus Ejrnæs

EU- ES- 001 Iberian and Macaronesian Vegetation Information System (SIVIM) ̶ Wetlands

6,560 Aaron Pérez- Haase Xavier Font

EU- FR- 003 SOPHY 209,864 Henry Brisse Patrice de Ruffray Brisse, de Ruffray,

Grandjouan, and Hoff (1995) EU- GB- 001 UK National Vegetation

Classification Database

28,533 John S. Rodwell

EU- GR- 001 KRITI 292 Erwin Bergmeier

EU- GR- 005 Hellenic Natura 2000 Vegetation Database (HelNatVeg)

5,168 Panayotis Dimopoulos Ioannis Tsiripidis Dimopoulos and Tsiripidis (2012) EU- GR- 006 Hellenic Woodland Database 3,199 Georgios Fotiadis Ioannis Tsiripidis Fotiadis, Tsiripidis,

Bergmeier, and Dimopoulos (2012) EU- HR- 001 Phytosociological Database of

Non- Forest Vegetation in Croatia

5,057 Zvjezdana Stančić Stančić (2012)

EU- HR- 002 Croatian Vegetation Database 8,734 Željko Škvorc Daniel Krstonošić EU- HU- 003 CoenoDat Hungarian

Phytosociological Database

8,505 János Csiky Zoltán Botta- Dukát Lájer et al. (2008)

EU- IT- 001 VegItaly 15,332 Roberto Venanzoni Flavia Landucci Landucci et al. (2012)

EU- IT- 010 Italian National Vegetation

Database (BVN/ISPRA) 3,562 Laura Casella Pierangela Angelini Casella, Bianco,

Angelini, and Morroni (2012) EU- IT- 011 Vegetation- Plot Database

Sapienza University of Rome (VPD- Sapienza)

12,780 Emiliano Agrillo Fabio Attorre Agrillo et al. (2017)

EU- LT- 001 Lithuanian Vegetation Database 7,821 Valerijus Rašomavičius Domas Uogintas EU- LV- 001 Semi- natural Grassland

Vegetation Database of Latvia

5,594 Solvita Rūsiņa Rūsiņa (2012)

EU- MK- 001 Vegetation Database of the

Republic of Macedonia 1,417 Renata Ćušterevska

EU- NL- 001 Dutch National Vegetation Database

102,327 Joop H.J. Schaminée Stephan M.

Hennekens

Schaminée et al.

(2006)

EU- PL- 001 Polish Vegetation Database 22,229 Zygmunt Kącki Grzegorz Swacha Kącki and Śliwiński (2012)

EU- RO- 007 Romanian Forest Database 6,017 Adrian Indreica Pavel Dan Turtureanu Indreica, Turtureanu, Szabó, and Irimia (2017)

EU- RO- 008 Romanian Grassland Database 1,921 Eszter Ruprecht Kiril Vassilev Vassilev et al. (2018) EU- RS- 002 Vegetation Database Grassland

Vegetation of Serbia 5,587 Svetlana Aćić Zora Dajić Stevanović Aćić, Petrović, Šilc, and Dajić Stevanović (2012) TA B L E   2  (Continued)

(Continues)

GIVD ID Database name

# of plots in

sPlot 2.1 Custodian Deputy custodian Reference

EU- RU- 002 Lower Volga Valley Phytosociological Database

14,853 Valentin Golub Viktoria Bondareva Golub et al. (2012)

EU- RU- 003 Vegetation Database of the Volga and the Ural Rivers Basins

1,516 Tatiana Lysenko Lysenko,

Mitroshenkova, and Kalmykova (2012) EU- RU- 011 Vegetation Database of

Tatarstan

7,471 Vadim Prokhorov Maria Kozhevnikova Prokhorov, Rogova, and Kozhevnikova (2017)

EU- SI- 001 Vegetation Database of Slovenia 10,986 Urban Šilc Filip Küzmič Šilc (2012)

EU- SK- 001 Slovak Vegetation Database 36,405 Milan Valachovič Jozef Šibík Šibík (2012)

EU- UA- 001 Ukrainian Grasslands Database 4,043 Anna Kuzemko Yulia Vashenyak Kuzemko (2012)

EU- UA- 006 Vegetation Database of Ukraine and Adjacent Parts of Russia

3,326 Viktor Onyshchenko Vitaliy Kolomiychuk

[Aggregator] Tropical African Vegetation Archive (TAVA)

6,677 Marco Schmidt Stefan Dressler Janßen et al. (2011)

AF- 00- 001 West African Vegetation

Database 3,129 Marco Schmidt Georg Zizka Schmidt et al. (2012)

AF- 00- 008 PANAF Vegetation Database 2,469 Hjalmar Kühl TeneKwetche Sop

AF- BF- 001 Sahel Vegetation Database 1,079 Jonas V. Müller Marco Schmidt Müller (2003)

Other databases 164,566

00- 00- 001 RAINFOR data managed by

ForestPlots.net 1,827 Oliver L. Phillips Aurora Levesley Lopez- Gonzalez,

Lewis, Burkitt, and Phillips (2011)

00- 00- 003 SALVIAS 4,883 Brian Enquist Brad Boyle

00- 00- 005 Tundra Vegetation Plots (TundraPlot)

577 Anne D. Bjorkman Sarah Elmendorf Elmendorf et al.

(2012) 00- RU- 002 Database of Masaryk

University’s Vegetation Research in Siberia

1,547 Milan Chytrý Chytrý (2012)

AF- 00- 003 BIOTA Southern Africa Biodiversity Observatories Vegetation Database

1,666 Norbert Jürgens Gerhard Muche Muche, Schmiedel,

and Jürgens (2012)

AF- 00- 006 SWEA- Dataveg 2,704 Miguel Alvarez Michael Curran

AF- 00- 009 Vegetation Database of the Okavango Basin

590 Rasmus Revermann Manfred Finckh Revermann et al.

(2016) AF- CD- 001 Forest Database of Central

Congo Basin

292 Elizabeth Kearsley Hans Verbeeck Kearsley et al. (2013)

AF- ET- 001 Vegetation Database of Ethiopia 74 Desalegn Wana Anke Jentsch Wana and

Beierkuhnlein (2011) AF- MA- 001 Vegetation Database of

Southern Morocco

1,337 Manfred Finckh Finckh (2012)

AF- ZA- 003* SynBioSys Fynbos Vegetation Database

3,810 John Janssen

AF- ZW- 001* Vegetation Database of Zimbabwe

36 Cyrus Samimi Samimi (2003)

AS- 00- 001 Korean Forest Database 4,885 Tomáš Černý Petr Petřík Černý et al. (2015)

AS- 00- 003 Vegetation of Middle Asia 1,381 Arkadiusz Nowak Marcin Nobis Nowak et al. (2017)

AS- 00- 004 Rice Field Vegetation Database 179 Arkadiusz Nowak TA B L E   2  (Continued)

(Continues)

GIVD ID Database name # of plots in

sPlot 2.1 Custodian Deputy custodian Reference

AS- BD- 001 Tropical Forest Dataset of

Bangladesh 211 Mohammed A.S. Arfin

Khan Fahmida Sultana

AS- CN- 001 China Forest- Steppe Ecotone Database

148 Hongyan Liu Fengjun Zhao Liu, Cui, Pott, and

Speier (2000) AS- CN- 002 Tibet- PaDeMoS Grazing

Transect

146 Karsten Wesche Wang et al. (2017)

AS- CN- 003* Vegetation Database of the BEF China Project

27 Helge Bruelheide Bruelheide et al.

(2011) AS- CN- 004* Vegetation Database of the

Northern Mountains in China

485 Zhiyao Tang

AS- CN- 005* Database Steppe Vegetation of

Xinjiang 129 Kohei Suzuki

AS- EG- 001 Vegetation Database of Sinai in Egypt

926 Mohamed Z. Hatim Hatim (2012)

AS- ID- 001 Sulawesi Vegetation Database 24 Michael Kessler

AS- IR- 001 Vegetation Database of Iran 2,335 Jalil Noroozi Parastoo Mahdavi

AS- KG- 001 Vegetation Database of South- Western Kyrgyzstan

452 Peter Borchardt Udo Schickhoff Borchardt and

Schickhoff (2012) AS- KZ- 001 Database of Meadow Vegetation

in the NW Tian Shan Mountains

94 Viktoria Wagner Wagner (2009)

AS- MN- 001 Southern Gobi Protected Areas Database

1,516 Henrik von Wehrden Karsten Wesche von Wehrden,

Wesche, and Miehe (2009)

AS- RU- 001 Wetland Vegetation Database of

Baikal Siberia (WETBS) 2,381 Victor Chepinoga Chepinoga (2012)

AS- RU- 002 Database of Siberian Vegetation (DSV)

9,116 Andrey Korolyuk Andrei Zverev

AS- RU- 004 Database of the University of Münster - Biodiversity and Ecosystem Research Group’s Vegetation Research in Western Siberia and Kazakhstan

445 Norbert Hölzel Wanja Mathar

AS- SA- 001* Vegetation Database of Saudi Arabia

919 Mohamed Abd

El- Rouf Mousa El- Sheikh

AS- TJ- 001 Eastern Pamirs 282 Kim André Vanselow Vanselow (2016)

AS- TW- 001 National Vegetation Database of

Taiwan 930 Ching- Feng Li Chang- Fu Hsieh

AS- YE- 001 Socotra Vegetation Database 396 Michele De Sanctis Fabio Attorre De Sanctis and Attorre (2012)

AU- AU- 002 TERN AEKOS 21,261 Anita Smyth Ben Sparrow Turner, Smyth,

Walker, and Lowe (2017)

AU- NC- 001 New Caledonian Plant Inventory and Permanent Plot Network (NC- PIPPN)

201 Jérôme Munzinger Philippe Birnbaum Ibanez et al. (2014)

AU- NZ- 001 New Zealand National Vegetation Databank

1,895 Susan Wiser Wiser, Bellingham,

and Burrows (2001) AU- PG- 001 Forest Plots from Papua New

Guinea

63 Timothy Whitfeld George Weiblen Whitfeld et al. (2014)

TA B L E   2  (Continued)

(Continues)

>25%. Similarly, we defined non- forests by calculating the cover of all taxa that were not defined as trees or shrubs (also taken from the TRY plant growth form information) and that were not taller than 2 m, using the TRY data on mean plant height. In total, 21,888 taxa belonged to this category. We defined all plots as non- forests if the sum of relative cover of these low- stature, non- tree and non- shrub taxa was >90%. As we did not have the growth form and height in- formation for all taxa, a fraction of about 25% of the plots remained unassigned (i.e., neither forest, nor non- forest). In addition, more detailed classifications of plots into physiognomic formations (Table S3.2 in Appendix S3) and naturalness (Table S3.3 in Appendix S3) were derived from various types of plot- level or database- level in- formation provided by the sources and stored in five separate fields (see Table S2.1 in Appendix S2).

2.4 | Phylogenetic information

We developed a workflow to generate a phylogeny of the vascu- lar plant species in sPlot, using the phylogeny of Zanne et al. (2014), updated by Qian and Jin (2016). Species present in sPlot but miss- ing from this phylogeny were added next to a randomly selected congener (see also Maitner et al., 2018). This approach has been demonstrated to introduce less bias into subsequent analyses than adding missing species as polytomies to the respective genera (Davies, Kraft, Salamin, & Wolkovich, 2012). We only added species based on taxonomic information on the genus level, thus not mak- ing use of family affiliation. Because of the absence of congeners in the reference phylogeny, 7,147 species could not be added (11.7%

of all resolved taxa in sPlot and TRY). This resulted in a phylogeny

GIVD ID Database name # of plots in

sPlot 2.1 Custodian Deputy custodian Reference

NA- 00- 002 Tree Biodiversity Network

(BIOTREE- NET) 1,757 Luis Cayuela Cayuela et al. (2012)

NA- CA- 003 Database of Timberline Vegetation in NW North America

110 Viktoria Wagner Toby Spribille Wagner, Spribille,

Abrahamczyk, and Bergmeier (2014) NA- CA- 004 Understory of Sugar Maple

Dominated Stands in Quebec and Ontario (Canada)

156 Isabelle Aubin Aubin, Gachet,

Messier, and Bouchard (2007)

NA- CA- 005* Boreal Forest of Canada 89 Yves Bergeron Louis De Grandpré

NA- GL- 001 Vegetation Database of

Greenland 664 Birgit Jedrzejek Fred J.A. Daniëls Sieg, Drees, and

Daniëls (2006)

NA- US- 002 VegBank 67,352 Robert K. Peet Michael T. Lee Peet et al. (2012)

NA- US- 006 Carolina Vegetation Survey Database

17,221 Robert K. Peet Michael T. Lee Peet et al. (2012)

NA- US- 014 Alaska- Arctic Vegetation Archive 1,363 Donald A. Walker Amy Breen Walker et al. (2016)

SA- 00- 002 VegPáramo 2,643 Gwendolyn Peyre Xavier Font Peyre et al. (2015)

SA- AR- 002 Vegetation Database of Central Argentina

218 Marcelo R. Cabido Alicia Acosta

SA- BO- 003 Bolivia Forest Plots 75 Michael Kessler Sebastian Herzog

SA- BR- 002 Forest Inventory, State of Santa Catarina, Brazil (IFFSC Project)

1,669 Alexander Christian Vibrans

André Luis de Gasper Vibrans, Sevegnani, Lingner, de Gasper, and Sabbagh (2010) SA- BR- 003 Grasslands of Rio Grande do Sul,

Brazil

320 Eduardo Vélez- Martin Valério De Patta Pillar

SA- BR- 004 Grassland Database of Campos

Sulinos 161 Gerhard E. Overbeck Valério De Patta Pillar

SA- CL- 002 SSAForests_Plots_db 261 Alvaro G. Gutierrez

SA- CL- 003* Chilean Park Transects - Fondecyt 1040528

165 Aníbal Pauchard Alicia Marticorena Pauchard, Fuentes, Jiménez, Bustamante, and Marticorena (2013) SA- EC- 001 Ecuador Forest Plot Database 172 Jürgen Homeier

Note. GIVD ID refers to the ID in the Global Index of Vegetation- Plot Databases (http://www.givd.info), which manages the metadata for sPlot and provides updated online descriptions of these databases; * after the GIVD ID indicates that the respective database description is currently not visible on the GIVD website. Datasets contributed in harmonized format from a continental data aggregator (“collective database” according to the sPlot Rules) are listed under its name. Further references, attributions and disclaimers for particular datasets are found Appendix S1.

TA B L E   2  (Continued)

with 54,067 resolved taxon names from 61,214 standardized taxa in the combined list of sPlot and TRY. The tree was finally pruned to the vascular plant taxa of the current sPlot version 2.1, resulting in a phylogenetic tree for 53,489 out of the 58,066 taxa in sPlot. Of these 53,489 names, 16,026 are also found among the 31,389 taxa in the phylogenetic tree of Qian and Jin (2016), i.e., 51.1%. The full procedure and the R code are available in Purschke (2017b).

2.5 | Associated environmental plot information

To complement the plot data, we harmonized geographical coor- dinates (in decimal degrees), elevation (m above sea level), aspect (degrees) and slope (degrees) as provided by the contributing data- bases. All other variables were too sparsely and too inconsistently sampled across databases to be combined in the global set, but were retained in the original data sources and can be retrieved for par- ticular purposes.

We used the geographic coordinates to create a geodatabase in ArcGIS 14.1 (ESRI, Redlands, CA) to link sPlot 2.1 to these climate and soil data. We retrieved data for all the 19 bioclimatic variables provided by CHELSA v1.1 (Karger et al., 2017) by averaging climatic data from the period 1979–2013 at 30 arc seconds (about 1 km in grid cells near to the equator). These variables are the same as the ones used in WorldClim (www.worldclim.org; Hijmans, Cameron, Parra, Jones, & Jarvis, 2005), but calculated with a downscaling approach based on estimates of the ERA- Interim climatic reanaly- sis (Dee et al., 2011). While the CHELSA climatological data have a similar accuracy as other products for temperature, they are more precise for precipitation patterns (Karger et al., 2017). We also

calculated growing degree days for 1°C (GDD1) and 5°C (GDD5), according to Synes and Osborne (2011) and based on CHELSA data, and included the index of aridity and potential evapotranspiration extracted from the CGIAR- CSI website (www.cgiar-csi.org). In addi- tion, we extracted seven soil variables from the SOILGRIDS project (https://soilgrids.org/; licensed by ISRIC – World Soil Information), downloaded at 250- m resolution and then converted to the same 30- arc second grid format of CHELSA. To explore the distribution of sPlot data in the global environmental space, we subjected all 30 climate and soil variables of the global terrestrial surface rasterized on a 2.5 arc- minute grid resolution to a principal component analysis (PCA) on standardized and centered data. We subsequently created a grid of 100 cells × 100 cells within the bi- dimensional environ- mental space defined by the first two PCA axes (PC1 and PC2) and counted the number of terrestrial cells per environmental grid cell of the PC1–PC2 space. Then, we counted the number of plots in sPlot in the same PCA grid (Figure 2).

We linked all vegetation plots to two global biome classifica- tions. We used the World Wildlife Fund (WWF) spatial informa- tion on terrestrial ecoregions (Olson et al., 2001) to assign plots to one of the 867 ecoregions, 14 biomes and eight biogeographic realms. The WWF approach is based on a bottom- up expert sys- tem using various regional biodiversity sources to define ecore- gions, which in turn are grouped into realms and biomes (Olson et al., 2001). In addition, we created a shapefile for the ecozones defined by Schultz (2005) to represent major biomes in response to global climatic variation. Since these zones are climatically hetero- geneous in mountain regions, we differentiated an additional “al- pine” biome for mountain areas above the lower mountain thermal

F I G U R E   2  Distribution of vegetation plots from sPlot 2.1 in the global environmental space. Comparison of the distribution of all terrestrial 2.5 arc- minute cells (a) and plots in sPlot 2.1 (b) in the principal component analysis (PCA) space defined on 30 environmental (climate and soil) variables. The PCA space was divided into a 100 × 100 regular grid. For each element of this grid, the graphs show the number of 2.5 arc- minute cells (a) and plots (b), respectively. Colors refer to the logarithm of number of plots, with the legend showing untransformed number of plots. The first and second PCA axis explained 48.6% and 27.3% of the total variance

belt, as defined in the classification of world mountain regions by Körner et al. (2017). This resulted in a distinction of 10 major bi- omes (Figure S4.5 in Appendix S4), whose shapefile is freely avail- able (Appendix S5).

2.6 | Trait information

To broaden the potential applications of the global vegetation da- tabase in functional contexts, we linked sPlot to TRY. We accessed plant trait data from TRY version 3.0 on August 10, 2016, and in- cluded 18 traits that describe the leaf, wood and seed economics spectra (Westoby, 1998; Reich, 2014; Table S6.4 in Appendix S6), and are known to affect different key ecosystem processes and to respond to macroclimatic drivers. These traits were represented across all species in the TRY database by at least 1,000 trait re- cords. We excluded trait records from manipulative experiments and outliers (Kattge et al., 2011), which resulted in a matrix with 632,938 individual plant records on 52,032 taxa in TRY, having data records for an average of 3.08 of the 18 selected traits. On average, each trait has been measured at least once in 17.1% of all taxa. In order to attain data for these 18 traits for all species with at least one trait value in TRY, we employed hierarchical Bayesian modeling, using the R package ‘BHPMF’ (Fazayeli, Banerjee, Kattge, Schrodt, & Reich, 2017; Schrodt et al., 2015), to fill a gap in the matrix of individual plant records in TRY. Gap filling allows obtain- ing trait values for a species on which this trait has not been meas- ured, but for which other traits are available. To assess gap- filling quality, we used the probability density distributions provided by BHPMF for each imputation and removed highly uncertain imputa- tions with a coefficient of variation >1. We then log_e- transformed all gap- filled trait values and averaged each trait by taxon. For taxa recorded at genus level only, we calculated genus means, result- ing in a full trait matrix for 26,632 out of the 54,519 taxa in sPlot (45.9%), with 6, 1,510 and 25,116 taxa at the family, genus and spe- cies level, respectively. These species covered 88.7% of all species- by- plot combinations.

For every trait j and plot k, we calculated the community- weighted mean (CWM) and the community- weighted variance (CWV) for each of the 18 traits in a plot (Enquist et al., 2015):

where n_k is the number of species with trait information in plot k, p_i,k is the relative abundance of species i in plot k calculated as the species’ fraction in cover or abundance of total cover or abundance, and t_i,j is the mean value of species i for trait j. CWMs and CWVs were calculated for 18 traits in 1,117,369 and 1,099,463 plots, re- spectively, the second being a smaller number as at least two taxa were needed for CWV calculation.

3  | CONTENT OF SPLOT 2.1

3.1 | Plot community data

sPlot 2.1 contains 1,121,244 vegetation plots from 160 countries and from all continents (Figure 3). The global coverage is biased towards Europe, North America and Australia, reflecting unequal sampling effort across the globe (Table 1). At the ecoregion level, major gaps occur in the wet tropics of South America and Asia, as well as in subtropical deserts worldwide and in the North American taiga. Although the plots are highly clustered geographically, their coverage in the environmental space is much more representative:

the highest concentration of plots is found in environments that are most abundant globally (Figure 2), while they are lacking in the very moist parts of the environmental space, which are also spatially rare, and in the very cold parts, which are sparsely vegetated.

In most cases (98.4%), plot records in sPlot include full species lists of vascular plants, while 1.6% had only wood species above a certain diameter or only the most dominant species recorded.

Terricolous bryophytes and lichens were additionally identified in 14% and 7% of plots, respectively (Table S2.1 in Appendix S2). Forest and non- forest plots comprise 330,873 (29.7%) and 513,035 (46.0%) of all plots in sPlot, respectively. In most cases, species abundance was estimated using different variants of the Braun- Blanquet cover–abundance scale (66%), followed by per- centage cover (15%) and 55 other numeric or ordinal scales. The temporal extent of the data spans from 1885 to 2015, but >94%

of vegetation plots were recorded later than 1960 (Figure S2.1 in Appendix S2). Almost all plots are georeferenced (1,120,686) and the majority of plots have location uncertainty of 10 m or less (Figure S2.2 in Appendix S2).

Vascular plant richness per plot ranges from 1 to 723 species (median = 17 species). The most frequent richness class is between 20 and 25 species (Figure S2.3 in Appendix S2). Plot size is reported in 65.4% of plots, ranging from <1 m² to 25 ha, with a median of 36 m². While forest plots have plot sizes ≥100 m², and in most cases

≤1,000 m², non- forest plots range between 5 and 100 m² (Figure S2.4 in Appendix S2). When using these size ranges, forest plots tend to be richer in species (Figure 4a). The fact that the gradient in richness found in our plots was at least one order of magnitude stronger than differences that could be expected by the differ- ences in plot size prompted us to produce the first global maps of plot- scale species richness, separately for forests and non- forests (Figure 4a). While plots with complete vascular species composition are largely lacking from the wet tropics, for the remaining biomes the plot- scale richness data do not show the typical latitudinal rich- ness gradient in either formation. Particularly species- rich forests are found in the wet subtropics (such as SE United States, Taiwan and the East coast of Australia) as well as in some mountainous re- gions of the nemoral and steppic biomes of Eurasia. Likewise, non- forest communities have a particularly high mean vascular plant species in mountainous regions of the nemoral and steppic biomes of Eurasia.

CWM_j,k=

n_k

∑

i

p_i,kt_i,j

CWV_j,k=

nk

∑

i

p_i,k(t_i,j−CWM_j,k)²

F I G U R E   3  Global coverage of sPlot 2.1. (a) Contributing databases identified by different colours with indication of the two data aggregators (EVA, TAVA) and a few particularly large individual databases; (b) available plot numbers per WWF Ecoregion; and (c) available plot density in grid cells of 100 km × 100 km